What begins as innovation can quickly become expectation.

For brands and retailers, the ability to deliver personalized experiences has become an expectation, with personalized emails and product recommendations now part of the foundations of personalization.

Consumers want businesses to provide tailored interactions, with 76% of them getting frustrated when this isn’t delivered.

Shoppers demand experiences that recognize their preferences, anticipate their needs, and reward their loyalty.

But the challenge lies in delivering this at scale.

Traditional approaches, like “customers who bought X are shown Y”, are blunt instruments in a world where consumer journeys span devices, channels, and contexts.

That’s the gap artificial intelligence fills.

AI personalization

Traditionally, personalization relied heavily on broad segmentation. Customers were grouped into categories like “loyal shopper”, “bargain hunter”, or “new visitor”. These groups would be sent generic variations of messaging.

AI personalization goes beyond segmentation, using real-time customer data to create experiences that resonate with an individual.

By processing vast amounts of behavioral, transactional, and contextual data, AI enables brands to scale personalization and make it more relevant than ever before.

In particular, AI enhances:

Real-time responsiveness, adjusting recommendations and content instantly based on browsing behavior.

Predictive analytics, anticipating needs before customers explicitly state them.

Omnichannel consistency, aligning personalization across websites, apps, email campaigns, and even in-store experiences.

AI personalization in practice

Lacoste 

Lacoste leverages AI technology from Enhencer to analyze customer data and browsing behavior. This includes past purchases, website visits, and interactions with Lacoste’s social media channels.

By analyzing this data, AI creates a detailed profile of each customer, allowing Lacoste to tailor their ad content and messaging accordingly.

With this approach, Lacoste delivers more relevant and engaging marketing, driving stronger customer connections and better engagement across their digital channels.

Sephora

Sephora uses AI to power their 24/7 chatbots on their website and app. These chatbots act like beauty experts answering a range of questions.

They provide personalized recommendations, such as advising customers on the perfect shade of foundation for their skin tone or offering details about product ingredients.

Also, Sephora’s chatbots offer assistance in multiple languages, ensuring a global audience feels welcome and understood.

Starbucks

Starbucks leverages AI to analyze customer data within their mobile app, such as past purchases, preferred drinks, typical order times, and visit frequency. By harnessing this data, Starbucks can craft highly personalized marketing campaigns.

For example, a customer may receive a push notification suggesting their favorite pumpkin spice latte on a chilly autumn morning, or their preferred iced coffee for a summer treat.

AI can also recommend new drinks or food based on past behavior, introducing a customer to a new pastry that complements their regular drink.

Additionally, Starbucks utilizes geolocation to further personalize promotions through the app. When customers walk near a Starbucks location, they’ll receive targeted push notifications offering discounts or reminding them to use accumulated loyalty points.

Navigating the pitfalls

Of course, AI personalization isn’t a simple plug-and-play tool. Brands that dive in without a strategy risk undermining the customer experience rather than enhancing it.

Two areas require particular attention: data quality and privacy compliance.

Data quality 

AI is only as good as the data it consumes. Incomplete, inconsistent, or siloed data can lead to irrelevant or even counterproductive personalization.

Brands need a single source of truth for customer data, and that’s where a customer data platform (CDP) becomes essential.

A CDP consolidates first-party data from multiple touchpoints (online, offline, mobile apps, CRM systems) and standardizes it for AI models.

Missing purchase history, incorrect product attributes, or disconnected behavioral data can cause AI to make flawed recommendations, eroding trust and reducing ROI.

Investing in a CDP, combined with data governance and regular audits ensures AI personalization is accurate, actionable, and scalable.

Privacy and compliance

Personalization depends on understanding individual behavior, which inherently involves sensitive customer data.

Brands must navigate GDPR, CCPA, and other global privacy frameworks with care.

That means obtaining explicit consent for data collection, being transparent about how data is used, and giving customers control over their personal information.

Beyond compliance, privacy-first practices also build consumer trust, a critical factor in the success of AI-driven personalization. Missteps can lead not only to fines and reputational damage but also to a long-term loss of customer engagement.

To sum up

AI personalization is about making customers feel seen, understood, and valued. 

The brands that succeed will be the ones that use AI thoughtfully: keeping their data clean, respecting privacy, and focusing on experiences that genuinely help.

Do that well, and personalization becomes the reason customers keep coming back.